Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering

23 Apr 2017Xin BingFlorentina BuneaYang NingMarten Wegkamp

This work introduces a novel estimation method, called LOVE, of the entries and structure of a loading matrix A in a sparse latent factor model X = AZ + E, for an observable random vector X in Rp, with correlated unobservable factors Z \in RK, with K unknown, and independent noise E. Each row of A is scaled and sparse. In order to identify the loading matrix A, we require the existence of pure variables, which are components of X that are associated, via A, with one and only one latent factor... (read more)

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